Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach

Abstract

BackgroundMicroRNAs (miRNAs) are small non-coding RNAs which play a key role in the post-transcriptional regulation of many genes. Elucidating miRNA-regulated gene networks is crucial for the understanding of mechanisms and functions of miRNAs in many biological processes, such as cell proliferation, development, differentiation and cell homeostasis, as well as in many types of human tumors. To this aim, we have recently presented the biclustering method HOCCLUS2, for the discovery of miRNA regulatory networks. Experiments on predicted interactions revealed that the statistical and biological consistency of the obtained networks is negatively affected by the poor reliability of the output of miRNA target prediction algorithms. Recently, some learning approaches have been proposed to learn to combine the outputs of distinct prediction algorithms and improve their accuracy. However, the application of classical supervised learning algorithms presents two challenges: i) the presence of only positive examples in datasets of experimentally verified interactions and ii) unbalanced number of labeled and unlabeled examples.ResultsWe present a learning algorithm that learns to combine the score returned by several prediction algorithms, by exploiting information conveyed by (only positively labeled/) validated and unlabeled examples of interactions. To face the two related challenges, we resort to a semi-supervised ensemble learning setting. Results obtained using miRTarBase as the set of labeled (positive) interactions and mirDIP as the set of unlabeled interactions show a significant improvement, over competitive approaches, in the quality of the predictions. This solution also improves the effectiveness of HOCCLUS2 in discovering biologically realistic miRNA:mRNA regulatory networks from large-scale prediction data. Using the miR-17-92 gene cluster family as a reference system and comparing results with previous experiments, we find a large increase in the number of significantly enriched biclusters in pathways, consistent with miR-17-92 functions.ConclusionThe proposed approach proves to be fundamental for the computational discovery of miRNA regulatory networks from large-scale predictions. This paves the way to the systematic application of HOCCLUS2 for a comprehensive reconstruction of all the possible multiple interactions established by miRNAs in regulating the expression of gene networks, which would be otherwise impossible to reconstruct by considering only experimentally validated interactions.

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

“…It should be mentioned that semi-supervised multi-view learning has also been successfully applied for gene network reconstruction combining the interactions predicted by a number of different inference methods [19]. In a similar work, an ensemble-based SSL approach has been proposed for the computational discovery of miRNA regulatory networks from large-scale predictions produced by different algorithms [31].…”

Systematic prediction of target genes and pathways in cervical cancer from microRNA expression data

“…Previous studies have proposed the use of a number of different methods (21,22), including a semi-supervised method (21). The semi-supervised method was mainly dependent on a support vector machine model, which used the experimentally confirmed database miRTarBase (23) as the control set and Tarbase (24) as the test set.…”

MiR-278-3p regulates pyrethroid resistance in Culex pipiens pallens

“…MiRNAs can bind to the 3′ untranslated region (3′UTR) of their targets and silence gene expression in a sequence specific manner (Qiu et al 2008). Since the discovery of miRNAs in Caenorhabditis elegans (Lee et al 1993), numerous studies have demonstrated their essential role in regulating development, cell differentiation, apoptosis, and other critical biological events in both animals and plants (Hewezi et al 2012; Pio et al 2014; Samaraweera et al 2014). …”